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- W1606768424 abstract "Activity recognition has attracted increasing attention in recent years due to its potential to enable a number of compelling context-aware applications. As most approaches rely on supervised learning methods, obtaining substantial amounts of labeled data is often an important bottle-neck for these approaches. In this paper, we present and explore a novel method for activity recognition from sparsely labeled data. The method is based on multi-instance learning allowing to significantly reduce the required level of supervision. In particular we propose several novel extensions of multi-instance learning to support different annotation strategies. The validity of the approach is demonstrated on two public datasets for three different labeling scenarios." @default.
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- W1606768424 date "2009-01-01" @default.
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- W1606768424 title "Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning" @default.
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- W1606768424 doi "https://doi.org/10.1007/978-3-642-01721-6_10" @default.
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